CIFR vs CNXC

Cipher Digital Inc. vs Concentrix Corporation — Valuation Comparison 2026

CIFR

Information Technology Services
Cipher Digital Inc.
Quality
4.3
out of 10
Value Trap
24
SAFE
Price
$24.59
Last close
Models
12/13
Active
VS

CNXC

Information Technology Services
Concentrix Corporation
Quality
6.5
out of 10
Value Trap
12
SAFE
Price
$26.48
Last close
Models
5/13
Active

Model-by-Model Comparison

ModelType CIFR Fair ValueCIFR Upside CNXC Fair ValueCNXC Upside
Bayesian DCF Intrinsic $3.81 -84.5% $145.56 +449.7%
Earnings Power Value Intrinsic $0.94 -94.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.53 -96.9% $6.75 -74.5%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for CIFR vs CNXC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CIFR vs CNXC — Which Stock Is More Undervalued?

CNXC scores higher with a 6.5/10 quality rating vs CIFR's 4.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Cipher Digital Inc. (CIFR) and Concentrix Corporation (CNXC) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

CIFR currently trades at $24.59 with a QOC of 4.3/10, while CNXC trades at $26.48 with a QOC of 6.5/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).